1. Technical Field
The present invention relates in general to a method and system for guiding a mobile robot and, in particular, to a method and system for guiding a mobile robot based on the surrounding environment. Still more particularly, the present invention relates to a method and system for guiding a mobile robot based on a hypothesis of an object's configuration derived from environmental data.
2. Description of the Related Art
Mobile robots are used in manufacturing plants to transport loads. Many such mobile robots either move along a given run guide, such as a rail, or provide a detector for detecting the run distance traveled along a predetermined route stored in the mobile robot to enable it to move based on detected values. Some mobile robots have detectors such as ultrasonic wave sensors and bumpers (fenders) to avoid obstacles in the path of the mobile robot.
More recently, independent mobile robots having a free range of motion have become available. These independent robots must be able to recognize the surrounding environment to enable them to safely and efficiently move. Consequently, different types of sensors must be provided to detect a plurality of different states to enable independent mobile robots to operate safely and efficiently.
The sensors of a mobile robot have characteristics that differ with the type of sensor. Consequently, it is possible that the configurations of the environment derived from data acquired by each of a plurality of sensors may be contradictory. The configuration of the environment includes artificial and natural objects that can be detected by independent mobile robots. For example, ultrasonic wave sensors cannot provide accurate distance information for objects having surfaces at an angle relative to the radiative direction of the ultrasonic wave greater than 90.degree.. Stereo cameras, however, can obtain distance information from such surface configurations. Thus, distance information differs with the type of sensor.
An algorithm is available which evaluates the reliability of environmental data obtained from sensors by a statistical analysis that resolves contradictions between sensor data. (A. Elfes, "Sonar-Based Real-World Mapping and Navigation," IEEE Journal of Robotics and Automation, Vol. RA-3, No. 3, pp 249-265, June 1987; H. F. Durrant-Whyte, "Sensor Models and Multisensor Integration," The Int'l Journal of Robotics Research, Vol. 7, No. 6 pp 97-113, Dec. 1988). However, these techniques only improve the reliability of sensor data and cannot resolve contradictions generated by information inferred from other sensor data. In other words, sensor data depends on the configuration of the scene, so reliability must be changed for each sensor with relation to the configuration of the scene.
An additional difficulty in guiding mobile robots is that some objects are very difficult to detect due to sensor characteristics. Even though sensor data increases with respect to an object, reliability in interpreting objects is not necessarily improved due to contradictions between interpretations of sensor data. Thus, it becomes impossible to accurately interpret the environment. In order to be interpreted accurately, the environment must be interpreted for each sensor. If a contradiction arises between interpretations of the environment generated from multiple sensors, it must be determined which sensor gives an accurate interpretation.
Even when information on an object is insufficient, active sensing has been proposed that produces highly reliable sensor information by predicting an environment from information obtained up to that time, acting based on predictions, and harmonizing predictions and actions by sensors (S. A. Stanfield, "A Robotic Perceptual System Utilizing Passive Vision and Active Touch," The Int'l Journal of Robotics Research, Vol. 7, No. 6, pp 138-161, Dec. 1988). This predictive method provides highly reliable sensor data from an object located outside the dynamic range of sensors or at a position that cannot be initially detected by presuming a position favorable for detection and moving a sensor to that position. However, this method requires a movement plan which enables a sensor to be moved to a position for optimal object detection. It is difficult to devise an effective movement plan from sensor data having low reliability, particularly at initial detection. Consequently, a movement plan which enhances detection prevents an independent mobile robot from following its original movement plan.
A similar concept called intentional sensing has been suggested, which determines the mechanism of sensing in conjunction with a plan for recognition (Masatoshi Ishikawa, "Problem of Sensor Fusion," Journal of Japan Robotics Academy, Vol. 8, No. 6, pp 735-742, Dec. 1990). This method suggests a detection structure in which detection plans are clearly predetermined for an environment and in which methods utilized to detect known objects are predetermined. This method is limited to the conceptual level, however, and no operative embodiment has been proposed.
Consequently, it would be desirable to provide a method and system for guiding an independent mobile robot safely and effectively based on environmental data obtained utilizing a plurality of different sensors.